openAI-math-dataset / lean /OAI /MeasureTheory /Falconer /Recursion /IntermediateProfiles.lean
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import OAI.MeasureTheory.Falconer.Recursion.ProfileGrid
import OAI.MeasureTheory.Falconer.Estimates.AllDimensionalOccupiedClasses
import OAI.MeasureTheory.Falconer.Estimates.RegularizationScales
namespace OAI
/-!
# Mass-profile comparison at every depth in arbitrary dimension
The interpolation loss is (d+1)*lambda. The block depths satisfy the
same numerical bracketing as in the planar case.
-/
namespace Falconer.Regularization
variable {d : ℕ}
open MeasureTheory
open scoped ENNReal
noncomputable def sourceProfile (Q : DyadicCube d) (μ : Measure (Space d))
(N B : ℕ) (p : CellPath d) : ℝ → ℝ :=
regularizedProfile (regularizationLeaves Q μ N) (regularizationLabel Q μ N B) N B (N/B+1) p
noncomputable def sourceClass (Q : DyadicCube d) (μ : Measure (Space d))
(N B : ℕ) (p : CellPath d) : Measure (Space d) :=
normalizedClass Q μ (regularizationLeaves Q μ N) (regularizationLabel Q μ N B)
(regularizationLabel Q μ N B p)
theorem sourceClass_grid_comparison (Q : DyadicCube d) (hQ : 0 < Q.side)
(μ : Measure (Space d)) [IsProbabilityMeasure μ] {N B : ℕ} (hN : 0 < N) (hB : 0 < B)
{p : CellPath d} (hp : p ∈ regularizationLeaves Q μ N)
{i : ℕ} (hi : i ≤ N/B+1) {P : CellPath d}
(hP : P ∈ (classLeaves (regularizationLeaves Q μ N) (regularizationLabel Q μ N B)
(regularizationLabel Q μ N B p)).image (List.take (blockDepth N B i))) :
(((2:ℝ)^N)^(-sourceProfile Q μ N B p (blockDepth N B i)))/(2:ℝ)^(N/B+2) ≤
(sourceClass Q μ N B p (Q.descendant P).carrier).toReal ∧
(sourceClass Q μ N B p (Q.descendant P).carrier).toReal ≤
(2:ℝ)^(N/B+2)*(((2:ℝ)^N)^(-sourceProfile Q μ N B p (blockDepth N B i))) := by
classical
rw [sourceProfile,regularizedProfile_reference_grid hN (show regularizationLeaves Q μ N ⊆ cubePaths d N from Finset.filter_subset _ _)
(regularizationLabel Q μ N B) hB p hi]
exact regularization_grid_mass_bounds Q hQ μ N hB hp hi hP
theorem profile_bracket_mass_bound {R C lam m₀ mₙ m₁ x₀ xₙ x₁ : ℝ}
(hR : 1 ≤ R) (hC : 0 < C) (hCL : C ≤ R^lam)
(hml : m₁-mₙ ≤ (d:ℝ)*lam) (hmu : mₙ-m₀ ≤ (d:ℝ)*lam)
(hlo : R^(-m₁)/C ≤ x₁) (hhi : x₀ ≤ C*R^(-m₀))
(hxl : x₁ ≤ xₙ) (hxu : xₙ ≤ x₀) :
R^(-((d:ℝ)+1)*lam)*R^(-mₙ) ≤ xₙ ∧ xₙ ≤ R^(((d:ℝ)+1)*lam)*R^(-mₙ) := by
have hRp : 0 < R := lt_of_lt_of_le zero_lt_one hR
constructor
· apply le_trans ?_ (hlo.trans hxl)
apply (le_div_iff₀ hC).mpr
calc
R^(-((d:ℝ)+1)*lam)*R^(-mₙ)*C ≤ R^(-((d:ℝ)+1)*lam)*R^(-mₙ)*R^lam :=
mul_le_mul_of_nonneg_left hCL (by positivity)
_ = R^(-(d:ℝ)*lam-mₙ) := by rw [← Real.rpow_add hRp,← Real.rpow_add hRp]; congr 1; ring
_ ≤ R^(-m₁) := Real.rpow_le_rpow_of_exponent_le hR (by linarith)
· apply le_trans (hxu.trans hhi)
calc
C*R^(-m₀) ≤ R^lam*R^(-m₀) := mul_le_mul_of_nonneg_right hCL (by positivity)
_ = R^(lam-m₀) := by rw [← Real.rpow_add hRp]; rfl
_ ≤ R^(((d:ℝ)+1)*lam-mₙ) := Real.rpow_le_rpow_of_exponent_le hR (by linarith)
_ = R^(((d:ℝ)+1)*lam)*R^(-mₙ) := Real.rpow_add hRp _ _
theorem sourceProfile_short_increment (Q : DyadicCube d) (μ : Measure (Space d))
{N B : ℕ} (hN : 0 < N) (hB : 0 < B) (p : CellPath d) {lam u v : ℝ}
(hwidth : (B:ℝ) ≤ lam*(N:ℝ)) (hu : 0 ≤ u) (huv : u ≤ v) (hv : v ≤ N)
(hshort : v-u ≤ B) :
sourceProfile Q μ N B p v-sourceProfile Q μ N B p u ≤ (d:ℝ)*lam := by
classical
have hh := regularizedProfile_increment hN (show regularizationLeaves Q μ N ⊆ cubePaths d N from Finset.filter_subset _ _)
(regularizationLabel Q μ N B) hB p hu huv hv
apply hh.2.trans
apply (div_le_iff₀ (by exact_mod_cast hN : 0 < (N:ℝ))).mpr
have hmul₁ := mul_le_mul_of_nonneg_left hwidth (Nat.cast_nonneg d)
have hmul₂ := mul_le_mul_of_nonneg_left hshort (Nat.cast_nonneg d)
nlinarith
theorem sourceClass_all_depth_comparison (Q : DyadicCube d) (hQ : 0 < Q.side)
(μ : Measure (Space d)) [IsProbabilityMeasure μ] {N B : ℕ} (hN : 0 < N) (hB : 0 < B)
{lam : ℝ} (hwidth : (B:ℝ) ≤ lam*(N:ℝ))
(hlarge : (2:ℝ)^(N/B+2) ≤ ((2:ℝ)^N)^lam)
{p : CellPath d} (hp : p ∈ regularizationLeaves Q μ N)
{n : ℕ} (hn : n ≤ N) {P : CellPath d}
(hP : P ∈ (classLeaves (regularizationLeaves Q μ N) (regularizationLabel Q μ N B)
(regularizationLabel Q μ N B p)).image (List.take n)) :
((2:ℝ)^N)^(-((d:ℝ)+1)*lam)*((2:ℝ)^N)^(-sourceProfile Q μ N B p n) ≤
(sourceClass Q μ N B p (Q.descendant P).carrier).toReal ∧
(sourceClass Q μ N B p (Q.descendant P).carrier).toReal ≤
((2:ℝ)^N)^(((d:ℝ)+1)*lam)*((2:ℝ)^N)^(-sourceProfile Q μ N B p n) := by
classical
let i := (N-n)/B
have hb := PlanarFalconer.blockDepth_brackets hn hB
change i < N/B+1 ∧ blockDepth N B (i+1) ≤ n ∧ n ≤ blockDepth N B i ∧
blockDepth N B i-n ≤ B ∧ n-blockDepth N B (i+1) ≤ B at hb
obtain ⟨q,hq,rfl⟩ := Finset.mem_image.mp hP
let ρ := sourceClass Q μ N B p
have : IsProbabilityMeasure ρ := regularization_class_probability Q hQ μ N B hp
have hgp := sourceClass_grid_comparison Q hQ μ hN hB hp (Nat.le_of_lt hb.1)
(Finset.mem_image.mpr ⟨q,hq,rfl⟩)
have hgm := sourceClass_grid_comparison Q hQ μ hN hB hp (by omega : i+1 ≤ N/B+1)
(Finset.mem_image.mpr ⟨q,hq,rfl⟩)
have hmono₁ : ρ (Q.descendant (q.take (blockDepth N B i))).carrier ≤
ρ (Q.descendant (q.take n)).carrier := by
apply measure_mono
have hh := descendant_subset_take Q hQ (q.take (blockDepth N B i)) n
simpa only [List.take_take,Nat.min_eq_left hb.2.2.1] using hh
have hmono₀ : ρ (Q.descendant (q.take n)).carrier ≤
ρ (Q.descendant (q.take (blockDepth N B (i+1)))).carrier := by
apply measure_mono
have hh := descendant_subset_take Q hQ (q.take n) (blockDepth N B (i+1))
simpa only [List.take_take,Nat.min_eq_left hb.2.1] using hh
have hml := sourceProfile_short_increment Q μ hN hB p hwidth
(Nat.cast_nonneg n) (Nat.cast_le.mpr hb.2.2.1)
(Nat.cast_le.mpr (Nat.sub_le N (i*B))) (by
simpa only [Nat.cast_sub hb.2.2.1] using
(show (((blockDepth N B i-n):ℕ):ℝ) ≤ (B:ℝ) by exact_mod_cast hb.2.2.2.1))
have hmu := sourceProfile_short_increment Q μ hN hB p hwidth
(Nat.cast_nonneg (blockDepth N B (i+1))) (Nat.cast_le.mpr hb.2.1) (Nat.cast_le.mpr hn) (by
simpa only [Nat.cast_sub hb.2.1] using
(show (((n-blockDepth N B (i+1)):ℕ):ℝ) ≤ (B:ℝ) by exact_mod_cast hb.2.2.2.2))
exact profile_bracket_mass_bound (one_le_pow₀ (by norm_num : (1:ℝ) ≤ 2))
(by positivity) hlarge hml hmu hgp.1 hgm.2
(ENNReal.toReal_mono (measure_ne_top ρ _) hmono₁)
(ENNReal.toReal_mono (measure_ne_top ρ _) hmono₀)
end Falconer.Regularization
end OAI